{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data-driven modeling of a Resistance-Capacitance network with Neural ODEs\n",
    "\n",
    "This tutorial performs the data-driven modeling of a network of capacitive agents that are coupled by resistive connections.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Energy balance in dissipative systems**\n",
    "\n",
    "In many physical systems, behaviors over time are driven by input-output energy balances as prescribed by the [First Law of Thermodynamics](https://en.wikipedia.org/wiki/First_law_of_thermodynamics); i.e.:\n",
    "$$\n",
    "\\Delta U = Q - W,\n",
    "$$\n",
    "where $U$ is internal energy, $Q$ is heat added to the system, and $W$ is work done by the system on it surroundings. This energy accounting scheme can be extended to many situations. For example, suppose we wish to track the temperature ($T$) of a system with some thermal capacity ($C$) through time ($t$). After recasting internal energy as $U = C \\cdot T$, this yields a time-dependent form of the First Law of Thermodynamics:\n",
    "$$\n",
    "\\frac{dT}{dt} = \\frac{1}{C}\\left( \\dot{Q} - \\dot{W} \\right),\n",
    "$$\n",
    "with the heating and work terms now in units of power. This provides a compact framework for analysis of the time dynamics of systems that interact with their surroundings through heating and/or mechanical work. \n",
    "\n",
    "Here, we're concerned with systems comprised of many interacting *capacitive agents* - that is, a network of agents with a First Law of Thermodynamics input/output energy balance. This is the foundation for many engineering applications, such as building thermal dynamics. Each closed volume in a building has some thermal capacitance and is coupled via heat flows - through walls, heating, ventilation, air conditioning, etc. - with other capacitive agents. The couplings between capacitive agents are analogous to resistors in a circuit, acting to dissipate power across its connection points. An isolated capacitive agent subject to resistive energy transfer forms a [resistance-capacitance ciruit, or *RC Ciruit*](https://en.wikipedia.org/wiki/RC_circuit). Many coupled agents form a *RC Network*.\n",
    "\n",
    "A resistively-driven single zone RC agent obeys the First Law energy balance as:\n",
    "$$\n",
    "\\frac{dT}{dt} = \\frac{1}{C} \\left( \\frac{1}{R}\\left( T_{ext} - T \\right) \\right),\n",
    "$$\n",
    "where $T_{ext}$ is the forcing temperature. Note that no work is performed by the system on its surroundings. In the networked setting, the resistive couplings are summed over all of the connections for each agent in the network:\n",
    "$$\n",
    "\\frac{dT_i}{dt} = \\frac{1}{C_i} \\left( A_{i,j} \\sum_j^n  \\frac{1}{R_{i,j}} \\left( T_j - T_i \\right)  \\right),\n",
    "$$\n",
    "where $n$ is the number of agents in the network, $i,j \\in \\{1, 2, \\dots , n\\}$,  $A_{i,j}$ is the $(0,1)$-*adjacency matrix* defining the network structure (pairwise connections), and $R_{i,j}$ is the resistance for the pairwise interaction.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Modeling with NeruoMANCER**\n",
    "\n",
    "Our task is to leverage the known structure of RC networks to estimate capacitances and coupling resistivities from data. We will use the NeruoMANCER package for this task; let's do our imports:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install (Colab only)\n",
    "Skip this step when running locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install neuromancer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x21d401c6830>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numpy + plotting utilities + ordered dicts\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import OrderedDict\n",
    "\n",
    "\n",
    "# Standard PyTorch imports\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# Neuromancer imports\n",
    "from neuromancer.psl.coupled_systems import *\n",
    "from neuromancer.dynamics import integrators, ode, physics, interpolation\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.system import Node, System\n",
    "from neuromancer.loggers import BasicLogger\n",
    "from neuromancer.trainer import Trainer\n",
    "\n",
    "# Fix seeds for reproducibility\n",
    "np.random.seed(200)\n",
    "torch.manual_seed(0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Generation**\n",
    "\n",
    "Let's generate some data from a 5-zone RC network using NeuroMANCER's built-in RC-Network simulator. First we define a graph structure for our RC network as an array of pairwise connections and then hand this to PSL's RC Network class. A notional diagram of the network is shown below: 5 agents are coupled in a network, all five are connected to an outdoor temperature and a unique heating source."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![RC-Net diagram](figs/rc_net.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj = np.array([[0,1],[0,2],[0,3],[1,0],[1,3],[1,4],[2,0],[2,3],[3,0],[3,1],[3,2],[3,4],[4,1],[4,3]]).T\n",
    "s = RC_Network(nx=5, adj=adj)\n",
    "nsim = 500\n",
    "sim, s_dev, s_test = [s.simulate(nsim=nsim) for _ in range(3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can quickly take a look at the evolution of zonal temperatures over the length of the timeseries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21d6c357730>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c357790>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c3577c0>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c3578b0>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c3579a0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sim['Time'],sim['X'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And also the external inputs to the system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21d6c452b90>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c452bf0>,\n",
       " <matplotlib.lines.Line2D at 0x21d1ff230a0>,\n",
       " <matplotlib.lines.Line2D at 0x21d1ff229b0>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c2e0b20>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c452e30>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sim['Time'],sim['U'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datasets are handled by PyTorch DataLoaders viw DictDatasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nstep = 10\n",
    "\n",
    "train_data = {'Y': sim['Y'], 'X': sim['X'], 'U': sim['U']}\n",
    "dev_data = {'Y': s_dev['Y'], 'X': s_dev['X'], 'U': s_dev['U']}\n",
    "test_data = {'Y': s_test['Y'], 'X': s_test['X'], 'U': s_test['U']}\n",
    "for d in [train_data, dev_data]:\n",
    "    d['X'] = d['X'].reshape(nsim//nstep, nstep, 5)\n",
    "    d['Y'] = d['Y'].reshape(nsim//nstep, nstep, 5)\n",
    "    d['U'] = d['U'].reshape(nsim//nstep, nstep, 6)\n",
    "    d['xn'] = d['X'][:, 0:1, :]\n",
    "\n",
    "train_dataset, dev_dataset, = [DictDataset(d, name=n) for d, n in zip([train_data, dev_data], ['train', 'dev'])]\n",
    "train_loader, dev_loader, test_loader = [DataLoader(d, batch_size=nsim//nstep, collate_fn=d.collate_fn, shuffle=True) for d in [train_dataset, dev_dataset, dev_dataset]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Construction\n",
    "\n",
    "Now that we have our data ready to go, let's construct our model for the RC Network system. We have many options for modeling such systems ranging from completely data-driven methods (like Dynamic Mode Decomposition), to data-driven equation-based methods (like Neural ODEs or Sparse Identification of Nonlinear Dynamics). Here, we wish to leverage the known structure of the system to construct a parameter-tuning problem in NeuroMANCER. To that end, we need to create our 5-zone ``building'' and all of the resistive connections that make up the network.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Agents** \n",
    "\n",
    "At the core of a networked dynamical system, we define *agents* as objects possessing a dynamical state that evolves according to an Ordinary Differential Equation (ODE). These can be rooms in a building, a drone in a swarm, etc. In this case, we wish to create five capacitive agents to represent our building from our dataset. Here, we define the zones to be a list of 5 `RCNode`, each with a trainable parameter $C$ - the capacitance for the agent, and a scaling factor. This scaling factor is useful for including if you have prior knowledge of the approximate time constant for the agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "zones = [physics.RCNode(C=nn.Parameter(torch.tensor(5.0)),scaling=1.0e-5) for i in range(5)]  # heterogeneous population w/ identical physics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each zone in this network has its own heater. We include these heaters in the model via a `SourceSink` node - essentially a placeholder object for connecting our zones to external inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "heaters = [physics.SourceSink() for i in range(5)] # define heaters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, each zone is also in contact with the ambient environment (the outdoors). We represent outside as an additional `SourceSink` agent and then concatenate these lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "outside = [physics.SourceSink()]  \n",
    "\n",
    "# join lists:\n",
    "agents = zones + heaters + outside"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we connect our agents together in a graph, we need to define a mapping between our agents in the list and the indices of their respective states in the dataset. For this, we use a quick helper function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OrderedDict([('T', 0)]), OrderedDict([('T', 1)]), OrderedDict([('T', 2)]), OrderedDict([('T', 3)]), OrderedDict([('T', 4)]), OrderedDict([('T', 5)]), OrderedDict([('T', 6)]), OrderedDict([('T', 7)]), OrderedDict([('T', 8)]), OrderedDict([('T', 9)]), OrderedDict([('T', 10)])]\n"
     ]
    }
   ],
   "source": [
    "map = physics.map_from_agents(agents)\n",
    "# Let's take a look at this 'map':\n",
    "print(map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Couplings**\n",
    "\n",
    "From our instantiation of the PSL RC-Network, we already have an adjacency list corresponding to the connections among the 5 zones in our building. To avoid the tedium of individually constructing resistive connections between these agents, let's use a helper function to do it for us:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assuming new <class 'neuromancer.dynamics.physics.DeltaTemp'> for each element in edge list.\n",
      "14\n",
      "DeltaTemp()\n",
      "[array([0, 1])]\n"
     ]
    }
   ],
   "source": [
    "# Helper function for constructing couplings based on desired edge physics and an edge list:\n",
    "def generate_parameterized_edges(physics,edge_list):\n",
    "    \"\"\"\n",
    "    Quick helper function to construct edge physics/objects from adj. list:\n",
    "    \"\"\"\n",
    "\n",
    "    couplings = []\n",
    "    if isinstance(physics,nn.Module): # is \"physics\" an instance or a class?\n",
    "        # If we're in here, we expect one instance of \"physics\" for all edges in edge_list (homogeneous edges)\n",
    "        physics.pins = edge_list\n",
    "        couplings.append(physics)\n",
    "        print(f'Broadcasting {physics} to all elements in edge list.')\n",
    "    else: \n",
    "        # If we're in here, we expect different \"physics\" for each edge in edge_list (heterogeneous edges)\n",
    "        for edge in edge_list:\n",
    "            agent = physics(R=nn.Parameter(torch.tensor(50.0)),pins=[edge])\n",
    "            couplings.append(agent)\n",
    "\n",
    "        print(f'Assuming new {physics} for each element in edge list.')\n",
    "\n",
    "    return couplings\n",
    "\n",
    "couplings = generate_parameterized_edges(physics.DeltaTemp,list(adj.T))    # Heterogeneous edges of same physics\n",
    "\n",
    "# What do we have so far?\n",
    "print(len(couplings))\n",
    "# Let's take a look at one:\n",
    "print(couplings[0])\n",
    "# What's it connecting?\n",
    "print(couplings[0].pins)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to add connections between our `SourceSink` agents (the outdoors and the heaters) and the `RCNode` agents with more `DeltaTemp` resistive connections. These are easier to bookkeep, so let's do it manually. Here, the pins argument is expecting a list of arrays that specify pairwise interactions in the format (sender, receiver). Be sure that this order is consistent, especially when defining custom agents and couplings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Couple w/ outside temp:\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[0,5]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[1,5]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[2,5]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[3,5]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[4,5]]))\n",
    "\n",
    "# Couple w/ individual sources:\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[0,6]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[1,7]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[2,8]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[3,9]]))\n",
    "couplings.append(physics.DeltaTemp(R=nn.Parameter(torch.tensor(50.0)),pins=[[4,10]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're ready to define our ODE System, integrator, and dynamics model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ode = ode.GeneralNetworkedODE(\n",
    "    map = map,\n",
    "    agents = agents,\n",
    "    couplings = couplings,\n",
    "    insize = s.nx+s.nu,\n",
    "    outsize = s.nx,\n",
    "    inductive_bias=\"compositional\")\n",
    "\n",
    "fx_int = integrators.RK2(model_ode, h=1.0)\n",
    "\n",
    "dynamics_model = System([Node(fx_int,['xn','U'],['xn'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loss and problem definitions**\n",
    "\n",
    "We define our loss to be a simple Mean-Squared Error between the reference trajectories ($X^{j}(t)$) and model trajectories ($\\hat{X}^{j}(t)$) evaluated at the collocation points $i$ for each $j$-batch:\n",
    "$$\n",
    "\\mathcal{L} = \\frac{1}{N_i \\cdot N_k} \\left( \\sum_i \\sum_j \\left( X^{j}(t_i) - \\hat{X}^{j}(t_i) \\right)^2\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "Number of parameters: 29\n"
     ]
    }
   ],
   "source": [
    "x = variable(\"X\")\n",
    "xhat = variable(\"xn\")[:, :-1, :]\n",
    "\n",
    "reference_loss = ((xhat == x)^2)\n",
    "reference_loss.name = \"ref_loss\"\n",
    "\n",
    "objectives = [reference_loss]\n",
    "constraints = []\n",
    "\n",
    "# create constrained optimization loss\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "\n",
    "# construct constrained optimization problem\n",
    "problem = Problem([dynamics_model], loss)\n",
    "\n",
    "optimizer = torch.optim.Adam(problem.parameters(), lr=0.01)\n",
    "logger = BasicLogger(args=None, savedir='test', verbosity=1,\n",
    "                     stdout=[\"dev_loss\",\"train_loss\"])\n",
    "\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    train_loader,\n",
    "    dev_loader,\n",
    "    test_data,\n",
    "    optimizer,\n",
    "    epochs=1000,\n",
    "    patience=20,\n",
    "    warmup=5,\n",
    "    eval_metric=\"dev_loss\",\n",
    "    train_metric=\"train_loss\",\n",
    "    dev_metric=\"dev_loss\",\n",
    "    test_metric=\"dev_loss\",\n",
    "    logger=logger,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0\ttrain_loss: 0.01284\tdev_loss: 0.05561\teltime:  0.25205\n",
      "epoch: 1\ttrain_loss: 0.01278\tdev_loss: 0.05536\teltime:  0.57704\n",
      "epoch: 2\ttrain_loss: 0.01272\tdev_loss: 0.05510\teltime:  0.84812\n",
      "epoch: 3\ttrain_loss: 0.01266\tdev_loss: 0.05485\teltime:  1.12015\n",
      "epoch: 4\ttrain_loss: 0.01260\tdev_loss: 0.05460\teltime:  1.37500\n",
      "epoch: 5\ttrain_loss: 0.01254\tdev_loss: 0.05435\teltime:  1.67538\n",
      "epoch: 6\ttrain_loss: 0.01248\tdev_loss: 0.05410\teltime:  1.93220\n",
      "epoch: 7\ttrain_loss: 0.01242\tdev_loss: 0.05385\teltime:  2.20841\n",
      "epoch: 8\ttrain_loss: 0.01236\tdev_loss: 0.05360\teltime:  2.49175\n",
      "epoch: 9\ttrain_loss: 0.01231\tdev_loss: 0.05335\teltime:  2.87653\n",
      "epoch: 10\ttrain_loss: 0.01225\tdev_loss: 0.05310\teltime:  3.13448\n",
      "epoch: 11\ttrain_loss: 0.01219\tdev_loss: 0.05285\teltime:  3.37705\n",
      "epoch: 12\ttrain_loss: 0.01213\tdev_loss: 0.05260\teltime:  3.66636\n",
      "epoch: 13\ttrain_loss: 0.01207\tdev_loss: 0.05236\teltime:  3.92337\n",
      "epoch: 14\ttrain_loss: 0.01201\tdev_loss: 0.05211\teltime:  4.16764\n",
      "epoch: 15\ttrain_loss: 0.01196\tdev_loss: 0.05187\teltime:  4.45761\n",
      "epoch: 16\ttrain_loss: 0.01190\tdev_loss: 0.05163\teltime:  4.73752\n",
      "epoch: 17\ttrain_loss: 0.01184\tdev_loss: 0.05138\teltime:  5.01089\n",
      "epoch: 18\ttrain_loss: 0.01178\tdev_loss: 0.05114\teltime:  5.28018\n",
      "epoch: 19\ttrain_loss: 0.01173\tdev_loss: 0.05090\teltime:  5.53978\n",
      "epoch: 20\ttrain_loss: 0.01167\tdev_loss: 0.05066\teltime:  5.81230\n",
      "epoch: 21\ttrain_loss: 0.01161\tdev_loss: 0.05042\teltime:  6.09818\n",
      "epoch: 22\ttrain_loss: 0.01156\tdev_loss: 0.05018\teltime:  6.35454\n",
      "epoch: 23\ttrain_loss: 0.01150\tdev_loss: 0.04994\teltime:  6.62604\n",
      "epoch: 24\ttrain_loss: 0.01145\tdev_loss: 0.04970\teltime:  6.88568\n",
      "epoch: 25\ttrain_loss: 0.01139\tdev_loss: 0.04947\teltime:  7.14948\n",
      "epoch: 26\ttrain_loss: 0.01133\tdev_loss: 0.04923\teltime:  7.42847\n",
      "epoch: 27\ttrain_loss: 0.01128\tdev_loss: 0.04900\teltime:  7.73237\n",
      "epoch: 28\ttrain_loss: 0.01122\tdev_loss: 0.04876\teltime:  8.00197\n",
      "epoch: 29\ttrain_loss: 0.01117\tdev_loss: 0.04853\teltime:  8.24394\n",
      "epoch: 30\ttrain_loss: 0.01112\tdev_loss: 0.04830\teltime:  8.50300\n",
      "epoch: 31\ttrain_loss: 0.01106\tdev_loss: 0.04806\teltime:  8.76868\n",
      "epoch: 32\ttrain_loss: 0.01101\tdev_loss: 0.04783\teltime:  9.04321\n",
      "epoch: 33\ttrain_loss: 0.01095\tdev_loss: 0.04760\teltime:  9.35119\n",
      "epoch: 34\ttrain_loss: 0.01090\tdev_loss: 0.04737\teltime:  9.62027\n",
      "epoch: 35\ttrain_loss: 0.01085\tdev_loss: 0.04715\teltime:  9.88775\n",
      "epoch: 36\ttrain_loss: 0.01079\tdev_loss: 0.04692\teltime:  10.14805\n",
      "epoch: 37\ttrain_loss: 0.01074\tdev_loss: 0.04669\teltime:  10.43815\n",
      "epoch: 38\ttrain_loss: 0.01069\tdev_loss: 0.04647\teltime:  10.69076\n",
      "epoch: 39\ttrain_loss: 0.01063\tdev_loss: 0.04624\teltime:  10.93361\n",
      "epoch: 40\ttrain_loss: 0.01058\tdev_loss: 0.04602\teltime:  11.16591\n",
      "epoch: 41\ttrain_loss: 0.01053\tdev_loss: 0.04579\teltime:  11.42002\n",
      "epoch: 42\ttrain_loss: 0.01048\tdev_loss: 0.04557\teltime:  11.69303\n",
      "epoch: 43\ttrain_loss: 0.01042\tdev_loss: 0.04535\teltime:  11.93455\n",
      "epoch: 44\ttrain_loss: 0.01037\tdev_loss: 0.04513\teltime:  12.24176\n",
      "epoch: 45\ttrain_loss: 0.01032\tdev_loss: 0.04491\teltime:  12.51075\n",
      "epoch: 46\ttrain_loss: 0.01027\tdev_loss: 0.04469\teltime:  12.82642\n",
      "epoch: 47\ttrain_loss: 0.01022\tdev_loss: 0.04447\teltime:  13.08317\n",
      "epoch: 48\ttrain_loss: 0.01017\tdev_loss: 0.04425\teltime:  13.34523\n",
      "epoch: 49\ttrain_loss: 0.01012\tdev_loss: 0.04404\teltime:  13.59660\n",
      "epoch: 50\ttrain_loss: 0.01007\tdev_loss: 0.04382\teltime:  13.83879\n",
      "epoch: 51\ttrain_loss: 0.01002\tdev_loss: 0.04361\teltime:  14.11305\n",
      "epoch: 52\ttrain_loss: 0.00997\tdev_loss: 0.04339\teltime:  14.36309\n",
      "epoch: 53\ttrain_loss: 0.00992\tdev_loss: 0.04318\teltime:  14.61886\n",
      "epoch: 54\ttrain_loss: 0.00987\tdev_loss: 0.04297\teltime:  14.89964\n",
      "epoch: 55\ttrain_loss: 0.00982\tdev_loss: 0.04275\teltime:  15.18928\n",
      "epoch: 56\ttrain_loss: 0.00977\tdev_loss: 0.04254\teltime:  15.44572\n",
      "epoch: 57\ttrain_loss: 0.00972\tdev_loss: 0.04233\teltime:  15.74823\n",
      "epoch: 58\ttrain_loss: 0.00967\tdev_loss: 0.04213\teltime:  15.98673\n",
      "epoch: 59\ttrain_loss: 0.00962\tdev_loss: 0.04192\teltime:  16.24532\n",
      "epoch: 60\ttrain_loss: 0.00957\tdev_loss: 0.04171\teltime:  16.51833\n",
      "epoch: 61\ttrain_loss: 0.00953\tdev_loss: 0.04150\teltime:  16.80284\n",
      "epoch: 62\ttrain_loss: 0.00948\tdev_loss: 0.04130\teltime:  17.07744\n",
      "epoch: 63\ttrain_loss: 0.00943\tdev_loss: 0.04109\teltime:  17.36433\n",
      "epoch: 64\ttrain_loss: 0.00938\tdev_loss: 0.04089\teltime:  17.63546\n",
      "epoch: 65\ttrain_loss: 0.00933\tdev_loss: 0.04068\teltime:  17.92005\n",
      "epoch: 66\ttrain_loss: 0.00929\tdev_loss: 0.04048\teltime:  18.22104\n",
      "epoch: 67\ttrain_loss: 0.00924\tdev_loss: 0.04028\teltime:  18.52142\n",
      "epoch: 68\ttrain_loss: 0.00919\tdev_loss: 0.04008\teltime:  18.78927\n",
      "epoch: 69\ttrain_loss: 0.00915\tdev_loss: 0.03988\teltime:  19.10708\n",
      "epoch: 70\ttrain_loss: 0.00910\tdev_loss: 0.03968\teltime:  19.37584\n",
      "epoch: 71\ttrain_loss: 0.00905\tdev_loss: 0.03948\teltime:  19.66826\n",
      "epoch: 72\ttrain_loss: 0.00901\tdev_loss: 0.03928\teltime:  19.95548\n",
      "epoch: 73\ttrain_loss: 0.00896\tdev_loss: 0.03909\teltime:  20.20967\n",
      "epoch: 74\ttrain_loss: 0.00892\tdev_loss: 0.03889\teltime:  20.49080\n",
      "epoch: 75\ttrain_loss: 0.00887\tdev_loss: 0.03869\teltime:  20.78600\n",
      "epoch: 76\ttrain_loss: 0.00883\tdev_loss: 0.03850\teltime:  21.04587\n",
      "epoch: 77\ttrain_loss: 0.00878\tdev_loss: 0.03830\teltime:  21.35855\n",
      "epoch: 78\ttrain_loss: 0.00874\tdev_loss: 0.03811\teltime:  21.64326\n",
      "epoch: 79\ttrain_loss: 0.00869\tdev_loss: 0.03792\teltime:  21.89392\n",
      "epoch: 80\ttrain_loss: 0.00865\tdev_loss: 0.03773\teltime:  22.15631\n",
      "epoch: 81\ttrain_loss: 0.00860\tdev_loss: 0.03754\teltime:  22.44438\n",
      "epoch: 82\ttrain_loss: 0.00856\tdev_loss: 0.03735\teltime:  22.67984\n",
      "epoch: 83\ttrain_loss: 0.00851\tdev_loss: 0.03716\teltime:  22.98433\n",
      "epoch: 84\ttrain_loss: 0.00847\tdev_loss: 0.03697\teltime:  23.25885\n",
      "epoch: 85\ttrain_loss: 0.00843\tdev_loss: 0.03678\teltime:  23.51535\n",
      "epoch: 86\ttrain_loss: 0.00838\tdev_loss: 0.03660\teltime:  23.83182\n",
      "epoch: 87\ttrain_loss: 0.00834\tdev_loss: 0.03641\teltime:  24.11719\n",
      "epoch: 88\ttrain_loss: 0.00830\tdev_loss: 0.03622\teltime:  24.35816\n",
      "epoch: 89\ttrain_loss: 0.00825\tdev_loss: 0.03604\teltime:  24.66478\n",
      "epoch: 90\ttrain_loss: 0.00821\tdev_loss: 0.03586\teltime:  24.91851\n",
      "epoch: 91\ttrain_loss: 0.00817\tdev_loss: 0.03567\teltime:  25.17670\n",
      "epoch: 92\ttrain_loss: 0.00813\tdev_loss: 0.03549\teltime:  25.48013\n",
      "epoch: 93\ttrain_loss: 0.00809\tdev_loss: 0.03531\teltime:  25.75203\n",
      "epoch: 94\ttrain_loss: 0.00804\tdev_loss: 0.03513\teltime:  26.02120\n",
      "epoch: 95\ttrain_loss: 0.00800\tdev_loss: 0.03495\teltime:  26.26277\n",
      "epoch: 96\ttrain_loss: 0.00796\tdev_loss: 0.03477\teltime:  26.53731\n",
      "epoch: 97\ttrain_loss: 0.00792\tdev_loss: 0.03459\teltime:  26.80352\n",
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     ]
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
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     ]
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     ]
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    {
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     "output_type": "stream",
     "text": [
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      "epoch: 975\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  268.08507\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 976\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  268.35431\n",
      "epoch: 977\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  268.63941\n",
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      "epoch: 980\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  269.45078\n",
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      "epoch: 982\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  269.96026\n",
      "epoch: 983\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  270.21789\n",
      "epoch: 984\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  270.49751\n",
      "epoch: 985\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  270.73008\n",
      "epoch: 986\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  271.00640\n",
      "epoch: 987\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  271.26856\n",
      "epoch: 988\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  271.57694\n",
      "epoch: 989\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  271.84506\n",
      "epoch: 990\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  272.09982\n",
      "epoch: 991\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  272.36507\n",
      "epoch: 992\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  272.64258\n",
      "epoch: 993\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  272.90798\n",
      "epoch: 994\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  273.17319\n",
      "epoch: 995\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  273.43967\n",
      "epoch: 996\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  273.71024\n",
      "epoch: 997\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  273.97709\n",
      "epoch: 998\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  274.23435\n",
      "epoch: 999\ttrain_loss: 0.00009\tdev_loss: 0.00016\teltime:  274.49947\n"
     ]
    }
   ],
   "source": [
    "best_model = trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualization**\n",
    "\n",
    "How did we do? Let's do a quick n-step rollout of the timeseries and compare with the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21d6c4f37f0>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c4f3820>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c4f3850>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c4f0760>,\n",
       " <matplotlib.lines.Line2D at 0x21d6c4f1960>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u = torch.from_numpy(s_test['U']).float()\n",
    "sol = torch.zeros((500,5))\n",
    "ic = torch.tensor(s_test['X'][0,:5])\n",
    "for j in range(sol.shape[0]-1):\n",
    "    if j==0:\n",
    "        sol[[0],:] = ic.float()\n",
    "        sol[[j+1],:] = fx_int(sol[[j],:],u[[j],:])\n",
    "    else:\n",
    "        sol[[j+1],:] = fx_int(sol[[j],:],u[[j],:])\n",
    "\n",
    "plt.plot(sol.detach().numpy(),label='model', color = 'black')\n",
    "plt.plot(s_test['X'][:,:5],label = 'data', color = 'red')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "neuromancer",
   "language": "python",
   "name": "neuromancer"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
